In vertebrates, telomeres consisting of repeat arrays of the canonical TTAGGG DNA sequence and their associated shelterin proteins reside at the ends of each chromosome to maintain genomic stability by preventing the ends from being recognized as DNA double strand breaks (Palm and de Lange, 2008). In most adult human somatic cells telomerase, a cellular reverse transcriptase that adds the repetitive DNA to telomeres is not detected. Therefore, telomeres progressively shorten with each cell division due to the “end replication problem” (Olovnikov, 1973; Watson, 1972). In humans, telomere shortening has been implicated as a risk factor for numerous diseases such as atherosclerosis, cancer, cardiovascular disease, diabetes mellitus, and liver cirrhosis (Fitzpatrick et al., 2007; Samani et al., 2001; Sampson et al., 2006; Shay, 2016; Wiemann et al., 2002). In addition, genetic diseases have been identified that have direct or indirect defects in the telomere maintenance machinery termed telomere spectrum disorders (or telomeropathies) (Holohan et al., 2014). Patients with these syndromes display accelerate telomere attrition and much shorter telomeres when compared with age-matched healthy controls (Armanios and Blackburn, 2012; Opresko and Shay, 2017).
Furthermore, it has been well demonstrated that it is the shortest telomeres, not average telomere length, that is able to activate the DNA-damage response and subsequently trigger an irreversible arrest of cell-cycle progression, cellular senescence (Fumagalli et al., 2012; Hemann et al., 2001; Herbig et al., 2004; von Zglinicki et al., 2005; Zou et al., 2004). Cellular senescence has been correlated with a variety of age-associated disease and may also serves as a potential tumor suppressor mechanism to protect genome integrity and prevent accumulation of oncogenic changes (Campisi, 2013). Furthermore, an increase in the percent of the shortest telomeres has been proposed to be a lifespan predictor in mammals (Vera et al., 2012). Thus, the load of the shortest telomeres may serve as a biomarker for telomere-associated aging disorders, including cancer.
Various methods have been developed for quantifying TL. Most of these analyses provide information on average TL (Montpetit et al., 2014; Nussey et al., 2014; Vera and Blasco, 2012). Southern blots of Terminal Restriction Fragment (TRF) analysis are considered to be the “gold standard” method for TL measurement by estimating the intensity and size distribution of the “telomeric smear” (Kimura et al., 2010). The TRF technique not only requires a large amount of genomic DNA, but due to the lower hybridization signal of the shortest telomeres, TRF underestimates information about the abundance of the shortest telomeres. The quantitative PCR (qPCR) TL measurement assay (Cawthon, 2002) has been widely used for high throughput (HT) testing to overcome the amount of the genomic DNA requirement for TRF and measures ratios of telomere signals to single copy gene signals. However, qPCR method only provides relative TL that is proportional to the average TL from a reference sample. In addition, the qPCR method is not suitable to quantify TL for cancer studies since most of cancer cells are aneuploidy (Holland and Cleveland, 2009).
Using live or fixed cells, TL can be measured by different Quantitative Fluorescence In Situ Hybridization (Q-FISH) methods. Although metaphase Q-FISH (Lansdorp et al., 1996) can detect TL from each chromosome, this method does not permit analyses on non-dividing cells, such as senescent cells or resting lymphocytes. Using resting or interphase cells Flow-FISH and HT Q-FISH are adapted for large scale studies to typically estimate mean TL of interphase cells. While these approaches are an improvement over Q-PCR, one disadvantage of these techniques is the probe not only binds to telomeric repeats but also interacts with non-specific components in the cytoplasm (Aubert et al., 2012; Wieser et al., 2006). HT Q-FISH is able to quantify each individual telomere signal in each nucleus, however, telomere clustering has been reported in lower eukaryotes (Gasser et al., 2004) and also in mammalian cells (Ramirez and Surralles, 2008). Therefore, the percentage of the shortest telomeres may be underestimated using HT Q-FISH. Importantly, probe hybridization kinetics does not permit robust quantitation of the shortest telomeres (<2-3 Kb).
Single telomere length analysis (STELA) (Baird et al., 2003) was designed to generate high resolution of TL measurements including the shortest telomeres on individual chromosomes. Using ligation and PCR based methods combined with Southern blot analysis STELA is able to provide detailed information about the abundance of the shortest telomeres but only on a specific subset of chromosome ends. This is one major limitation of STELA. The Universal STELA (U-STELA) (Bendix et al., 2010) method was reported to detect telomeres from each chromosome using a suppression PCR strategy to prevent the amplification of the intra-genomic DNA fragments. However, this suppression PCR method was designed for DNA with low molecular weight (less than 500 bp) (Lavrentieva et al., 1999). It is not sufficient to suppress the amplification of larger genomic DNA fragments. In addition, U-STELA is not efficient to detect TL over 8 kb (Bendix et al., 2010) which could affect the detection of accuracy of TL distribution. Finally, U-STELA detects interstitial telomeric repeats and thus affecting the accuracy of TL distributions.
The present disclosure provides methods and compositions to accurately detect the shortest telomeres among a plurality of telomeres.